In this paper we studied three binary translations of non-binary
CSPs; the hidden variable encoding, the dual encoding, and the
double encoding. We showed that the common perception that
standard algorithms for binary CSPs can be used in the encodings
of non-binary CSPs suffers from flaws. Namely, standard algorithms
do not exploit the structure of the encodings, and end up being
inefficient. To address this problem, we proposed specialized arc
consistency and search algorithms for the encodings, and we
evaluated them theoretically and empirically. We showed how arc
consistency can be enforced on the hidden variable encoding of a
non-binary CSP with the same worst-case time complexity as
generalized arc consistency on the non-binary representation. We
showed that the structure of constraints in the dual encoding can
be exploited to achieve a much lower time complexity than a
generic algorithm. Empirical results demonstrated that the use of
a specialized algorithm makes the dual encoding significantly more
efficient. We showed that generalized search algorithms for
non-binary CSPs can be relatively easily adjusted to operate in
the hidden variable encoding. We also showed how various
algorithms for the double encoding can be designed. These
algorithms can exploit the properties of the double encoding
(strong filtering and branching on original variables) to achieve
very good results in certain problems. Empirical results in random
and structured problems showed that, for certain classes of
non-binary constraints, using binary encodings is a competitive
option, and in many cases, a better one than solving the
non-binary representation.